Various types of expert diagnostic systems provide standard test sequences and/or suggestions on diagnosing machines or patients. For example, a medical expert system solicits input related to a patient's symptoms and provides assistance on finding causes of the symptoms and cures thereof. Some expert diagnostic systems provide expert suggestions on what types of tests should be conducted to isolate sources of problems, and what types of fixes are available for such problems. These expert suggestions are created by a group of experienced experts or technicians, and implemented as software or control codes to be executed by the diagnostic systems.
Such type of expert diagnostic systems has its limitations and drawbacks. For a new model of machine or vehicle, it usually takes months or years for problems to develop, and for experts to become familiar with the machines or vehicles, and their problems. Thus, it usually takes a long time before an expert diagnostic system can be developed for a specific model of machine or vehicle.
In addition, the effectiveness of an expert diagnostic system is limited to the source of experts who created the system. For example, if an expert diagnostic system is created in Arizona, the experts are likely to be from that geographic area and thus problems they have encountered tend to be homogeneous. Thus, although an expert diagnostic system created by such experts may work well to solve problems for machines or vehicles in areas having weather conditions similar to those of Arizona, the conventional expert diagnostic system may not have sufficient expertise to solve problems for machines operated under different weather conditions, such as Alaska.
Moreover, the wisdom of conventional expert diagnostic systems is also limited to the small number of experts who created the expert diagnostic systems. Given the limited number of experts participated in the development of conventional expert diagnostic systems, it is unlikely that a conventional expert diagnostic system would be sufficiently sophisticated to address different types of problems effectively. Furthermore, after an expert diagnostic system is available, it usually takes a long time to develop updates for the systems and distribute the same to consumers.
Therefore, there is a need to design an expert diagnostic system with wisdom from as many experts as possible. There is another need to have an expert diagnostic system designed by experts having diverse backgrounds in order to address as many-types of problems as possible. There is also a need to provide expert suggestions for new models of machines or vehicles as soon as possible. An additional need exists to automate the process for collecting wisdom from different experts and to implement such wisdom into the expert diagnostic system dynamically.